EpiCompare compares epigenetic datasets for quality control and benchmarking purposes. The report consists of three sections:
## [1] "File1: H3K9me1_EN4G38_E1_IRpseudo1"
## [1] "File2: H3K9me1_EN3G38_E1_IRpseudo1"
## [1] "File3: H3K9me1_EN3G38_E1_IR1"
EpiCompare(peakfiles = list(H3K9me1_EN4G38_E1_IRpseudo1, H3K9me1_EN3G38_E1_IRpseudo1, H3K9me1_EN3G38_E1_IR1),
genome_build = hg38,
blacklist = blacklist,
picard_files = list(),
reference = H3K9me1_EN3G38_E1_IR1,
stat_plot = TRUE,
chromHMM_plot = TRUE,
chromHMM_annotation = "K562",
chipseeker_plot = TRUE,
enrichment_plot = TRUE,
interact = TRUE,
save_output = TRUE,
output_dir = "/Users/apple/Downloads/cutandrun/ENCODE_correlation/K562_H3K9me1")
Column Description:
BRGenomics::tidyChromosomes().NB: EpiCompare uses filtered peakfiles (i.e. datasets after removing peaks in blacklisted regions and non-standard chromosomes)
| Sample | PeakN before tidy | Blacklisted peaks removed (%) | Non-standard peaks removed (%) | PeakN after tidy |
|---|---|---|---|---|
| H3K9me1_EN4G38_E1_IRpseudo1 | 10062 | 0.0000 | 0.0000 | 10062 |
| H3K9me1_EN3G38_E1_IRpseudo1 | 4234 | 0.0236 | 0.0236 | 4232 |
Metrics on fragments is shown only if Picard summary is provided. See manual for help.
Column Description:
Distribution of peak widths in samples
Percentage of overlapping peaks between samples. Hover over heatmap for percentage values.
N.B. How to interpret heatmap: [Samples in x-axis of heatmap] peaks in [Samples in y-axis of heatmap] peaks
Upset plot of overlapping peaks between samples. See here on how to interpret the plot.
quartz_off_screen
2
Depending on the format of the reference file, EpiCompare outputs different plots:
Reference peakfile: H3K9me1_EN3G38_E1_IR1
Keys:
ChromHMM annotates and characterises peaks into different chromatin states. ChromHMM annotations used in EpiCompare were obtained from here.
ChromHMM annotation of individual samples.
Percentage of Sample peaks found in Reference peaks (Reference peakfile: H3K9me1_EN3G38_E1_IR1)
| Percentage | |
|---|---|
| H3K9me1_EN4G38_E1_IRpseudo1 | 53.4 |
| H3K9me1_EN3G38_E1_IRpseudo1 | 100.0 |
| H3K9me1_EN3G38_E1_IR1 | 100.0 |
ChromHMM annotation of sample peaks found in reference peaks.
Percentage of Reference peaks found in Sample peaks (Reference peakfile: H3K9me1_EN3G38_E1_IR1)
| Percentage | |
|---|---|
| H3K9me1_EN4G38_E1_IRpseudo1 | 36.4 |
| H3K9me1_EN3G38_E1_IRpseudo1 | 27.4 |
| H3K9me1_EN3G38_E1_IR1 | 100.0 |
ChromHMM annotation of reference peaks found in sample peaks.
Percentage of sample peaks not found in reference peaks (Reference peakfile: H3K9me1_EN3G38_E1_IR1)
| Percentage | |
|---|---|
| H3K9me1_EN4G38_E1_IRpseudo1 | 46.6 |
| H3K9me1_EN3G38_E1_IRpseudo1 | 0.0 |
| H3K9me1_EN3G38_E1_IR1 | 0.0 |
ChromHMM annotation of sample peaks not found in reference peaks.
Percentage of reference peaks not found in sample peaks (Reference peakfile: H3K9me1_EN3G38_E1_IR1)
| Percentage | |
|---|---|
| H3K9me1_EN4G38_E1_IRpseudo1 | 63.6 |
| H3K9me1_EN3G38_E1_IRpseudo1 | 72.6 |
| H3K9me1_EN3G38_E1_IR1 | 0.0 |
ChromHMM annotation of reference peaks not found in sample peaks.
EpiCompare uses ChIPseeker::annotatePeak() to annotate
peaks with the nearest gene and genomic regions where the peak is
located. The peaks are annotated with genes taken from human genome
annotations (hg19 or hg38) provided by Bioconductor.
EpiCompare performs KEGG pathway and GO enrichment analysis using
clusterProfiler. ChIPseeker::annotatePeak() is
first used to assign peaks to nearest genes. Biological themes amongst
the genes are identified using ontologies (KEGG and GO). The peaks are
annotated with genes taken from annotations of human genome (hg19 or
hg38) provided by Bioconductor.
This plots peaks that are mapping to transcriptional start sites (TSS). TSS regions are defined as the flanking sequence of the TSS sites. The frequency of peaks in downstream (-3000bp) and upstream (+3000bp) of TSS is plotted. Faint color line around the main frequency line represents the 95% confidence interval estimated by bootstrap method.
## >> Running bootstrapping for tag matrix... 2022-06-08 00时56分42秒
## >> Running bootstrapping for tag matrix... 2022-06-08 00时56分49秒
## >> Running bootstrapping for tag matrix... 2022-06-08 00时57分03秒
## [[1]]
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## list()
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##
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## [[2]]
## R version 4.2.0 (2022-04-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Monterey 12.3.1
##
## Matrix products: default
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
##
## locale:
## [1] zh_CN.UTF-8/zh_CN.UTF-8/zh_CN.UTF-8/C/zh_CN.UTF-8/zh_CN.UTF-8
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] rtracklayer_1.56.0 GenomicRanges_1.48.0 GenomeInfoDb_1.32.2 IRanges_2.30.0 S4Vectors_0.34.0 BiocGenerics_0.42.0
## [7] EpiCompare_1.0.0
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.2 tidyselect_1.1.2 RSQLite_2.2.14
## [4] AnnotationDbi_1.58.0 htmlwidgets_1.5.4 grid_4.2.0
## [7] BiocParallel_1.30.3 scatterpie_0.1.7 munsell_0.5.0
## [10] codetools_0.2-18 colorspace_2.0-3 GOSemSim_2.22.0
## [13] TxDb.Hsapiens.UCSC.hg38.knownGene_3.15.0 Biobase_2.56.0 filelock_1.0.2
## [16] highr_0.9 knitr_1.39 rstudioapi_0.13
## [19] DOSE_3.22.0 labeling_0.4.2 MatrixGenerics_1.8.0
## [22] GenomeInfoDbData_1.2.8 polyclip_1.10-0 seqPattern_1.28.0
## [25] bit64_4.0.5 farver_2.1.0 downloader_0.4
## [28] vctrs_0.4.1 treeio_1.20.0 generics_0.1.2
## [31] xfun_0.31 BiocFileCache_2.4.0 R6_2.5.1
## [34] graphlayouts_0.8.0 locfit_1.5-9.5 bitops_1.0-7
## [37] BRGenomics_1.8.0 cachem_1.0.6 fgsea_1.22.0
## [40] gridGraphics_0.5-1 DelayedArray_0.22.0 assertthat_0.2.1
## [43] vroom_1.5.7 promises_1.2.0.1 BiocIO_1.6.0
## [46] scales_1.2.0 ggraph_2.0.5 enrichplot_1.16.1
## [49] gtable_0.3.0 tidygraph_1.2.1 rlang_1.0.2
## [52] genefilter_1.78.0 splines_4.2.0 lazyeval_0.2.2
## [55] impute_1.70.0 plyranges_1.16.0 BiocManager_1.30.18
## [58] yaml_2.3.5 reshape2_1.4.4 crosstalk_1.2.0
## [61] GenomicFeatures_1.48.3 httpuv_1.6.5 qvalue_2.28.0
## [64] clusterProfiler_4.4.2 tools_4.2.0 ggplotify_0.1.0
## [67] gridBase_0.4-7 ggplot2_3.3.6 ellipsis_0.3.2
## [70] gplots_3.1.3 jquerylib_0.1.4 RColorBrewer_1.1-3
## [73] Rcpp_1.0.8.3 plyr_1.8.7 progress_1.2.2
## [76] zlibbioc_1.42.0 purrr_0.3.4 RCurl_1.98-1.6
## [79] prettyunits_1.1.1 viridis_0.6.2 SummarizedExperiment_1.26.1
## [82] ggrepel_0.9.1 magrittr_2.0.3 data.table_1.14.2
## [85] DO.db_2.9 matrixStats_0.62.0 evaluate_0.15
## [88] hms_1.1.1 patchwork_1.1.1 mime_0.12
## [91] xtable_1.8-4 XML_3.99-0.9 gridExtra_2.3
## [94] compiler_4.2.0 biomaRt_2.52.0 tibble_3.1.7
## [97] KernSmooth_2.23-20 crayon_1.5.1 shadowtext_0.1.2
## [100] htmltools_0.5.2 ggfun_0.0.6 later_1.3.0
## [103] tzdb_0.3.0 tidyr_1.2.0 geneplotter_1.74.0
## [106] aplot_0.1.6 DBI_1.1.2 tweenr_1.0.2
## [109] ChIPseeker_1.32.0 genomation_1.28.0 dbplyr_2.2.0
## [112] MASS_7.3-57 rappdirs_0.3.3 boot_1.3-28
## [115] Matrix_1.4-1 readr_2.1.2 cli_3.3.0
## [118] parallel_4.2.0 igraph_1.3.1 pkgconfig_2.0.3
## [121] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2 GenomicAlignments_1.32.0 plotly_4.10.0
## [124] xml2_1.3.3 ggtree_3.4.0 annotate_1.74.0
## [127] bslib_0.3.1 XVector_0.36.0 yulab.utils_0.0.4
## [130] stringr_1.4.0 digest_0.6.29 Biostrings_2.64.0
## [133] rmarkdown_2.14 fastmatch_1.1-3 tidytree_0.3.9
## [136] restfulr_0.0.14 curl_4.3.2 shiny_1.7.1
## [139] Rsamtools_2.12.0 gtools_3.9.2.1 rjson_0.2.21
## [142] lifecycle_1.0.1 nlme_3.1-157 jsonlite_1.8.0
## [145] viridisLite_0.4.0 BSgenome_1.64.0 fansi_1.0.3
## [148] pillar_1.7.0 lattice_0.20-45 KEGGREST_1.36.0
## [151] fastmap_1.1.0 httr_1.4.3 plotrix_3.8-2
## [154] survival_3.3-1 GO.db_3.15.0 interactiveDisplayBase_1.34.0
## [157] glue_1.6.2 UpSetR_1.4.0 png_0.1-7
## [160] BiocVersion_3.15.2 bit_4.0.4 sass_0.4.1
## [163] ggforce_0.3.3 stringi_1.7.6 blob_1.2.3
## [166] DESeq2_1.36.0 org.Hs.eg.db_3.15.0 AnnotationHub_3.4.0
## [169] caTools_1.18.2 memoise_2.0.1 dplyr_1.0.9
## [172] ape_5.6-2